Shai Avidan

ICLR Conference 2025 Conference Paper

CapeX: Category-Agnostic Pose Estimation from Textual Point Explanation

• Matan Rusanovsky
• Or Hirschorn
• Shai Avidan

Conventional 2D pose estimation models are constrained by their design to specific object categories. This limits their applicability to predefined objects. To overcome these limitations, category-agnostic pose estimation (CAPE) emerged as a solution. CAPE aims to facilitate keypoint localization for diverse object categories using a unified model, which can generalize from minimal annotated support images. Recent CAPE works have produced object poses based on arbitrary keypoint definitions annotated on a user-provided support image. Our work departs from conventional CAPE methods, which require a support image, by adopting a text-based approach instead of the support image. Specifically, we use a pose-graph, where nodes represent keypoints that are described with text. This representation takes advantage of the abstraction of text descriptions and the structure imposed by the graph. Our approach effectively breaks symmetry, preserves structure, and improves occlusion handling. We validate our novel approach using the MP-100 benchmark, a comprehensive dataset covering over 100 categories and 18,000 images. MP-100 is structured so that the evaluation categories are unseen during training, making it especially suited for CAPE. Under a 1-shot setting, our solution achieves a notable performance boost of 1.26\%, establishing a new state-of-the-art for CAPE. Additionally, we enhance the dataset by providing text description annotations for both training and testing. We also include alternative text annotations specifically for testing the model's ability to generalize across different textual descriptions, further increasing its value for future research. Our code and dataset are publicly available at https://github.com/matanr/capex.

ICLR Conference 2025 Conference Paper

Lightning-Fast Image Inversion and Editing for Text-to-Image Diffusion Models

• Dvir Samuel
• Barak Meiri
• Haggai Maron
• Yoad Tewel
• Nir Darshan
• Shai Avidan
• Gal Chechik
• Rami Ben-Ari

Diffusion inversion is the problem of taking an image and a text prompt that describes it and finding a noise latent that would generate the exact same image. Most current deterministic inversion techniques operate by approximately solving an implicit equation and may converge slowly or yield poor reconstructed images. We formulate the problem by finding the roots of an implicit equation and devlop a method to solve it efficiently. Our solution is based on Newton-Raphson (NR), a well-known technique in numerical analysis. We show that a vanilla application of NR is computationally infeasible while naively transforming it to a computationally tractable alternative tends to converge to out-of-distribution solutions, resulting in poor reconstruction and editing. We therefore derive an efficient guided formulation that fastly converges and provides high-quality reconstructions and editing. We showcase our method on real image editing with three popular open-sourced diffusion models: Stable Diffusion, SDXL-Turbo, and Flux with different deterministic schedulers. Our solution, **Guided Newton-Raphson Inversion**, inverts an image within 0.4 sec (on an A100 GPU) for few-step models (SDXL-Turbo and Flux.1), opening the door for interactive image editing. We further show improved results in image interpolation and generation of rare objects.

NeurIPS Conference 2025 Conference Paper

Optimize the Unseen - Fast NeRF Cleanup with Free Space Prior

• Leo Segre
• Shai Avidan

Neural Radiance Fields (NeRF) have advanced photorealistic novel view synthesis, but their reliance on photometric reconstruction introduces artifacts, commonly known as "floaters". These artifacts degrade novel view quality, particularly in unseen regions where NeRF optimization is unconstrained. We propose a fast, post-hoc NeRF cleanup method that eliminates such artifacts by enforcing a Free Space Prior, ensuring that unseen regions remain empty while preserving the structure of observed areas. Unlike existing approaches that rely on Maximum Likelihood (ML) estimation or complex, data-driven priors, our method adopts a Maximum-a-Posteriori (MAP) approach with a simple yet effective global prior. This enables our method to clean artifacts in both seen and unseen areas, significantly improving novel view quality even in challenging scene regions. Our approach generalizes across diverse NeRF architectures and datasets while requiring no additional memory beyond the original NeRF. Compared to state-of-the-art cleanup methods, our method is 2. 5x faster in inference and completes cleanup training in under 30 seconds. Our code will be made publicly available.

AAAI Conference 2024 Conference Paper

Optimize & Reduce: A Top-Down Approach for Image Vectorization

• Or Hirschorn
• Amir Jevnisek
• Shai Avidan

Vector image representation is a popular choice when editability and flexibility in resolution are desired. However, most images are only available in raster form, making raster-to-vector image conversion (vectorization) an important task. Classical methods for vectorization are either domain-specific or yield an abundance of shapes which limits editability and interpretability. Learning-based methods, that use differentiable rendering, have revolutionized vectorization, at the cost of poor generalization to out-of-training distribution domains, and optimization-based counterparts are either slow or produce non-editable and redundant shapes. In this work, we propose Optimize & Reduce (O&R), a top-down approach to vectorization that is both fast and domain-agnostic. O&R aims to attain a compact representation of input images by iteratively optimizing Bezier curve parameters and significantly reducing the number of shapes, using a devised importance measure. We contribute a benchmark of five datasets comprising images from a broad spectrum of image complexities - from emojis to natural-like images. Through extensive experiments on hundreds of images, we demonstrate that our method is domain agnostic and outperforms existing works in both reconstruction and perceptual quality for a fixed number of shapes. Moreover, we show that our algorithm is x10 faster than the state-of-the-art optimization-based method. Our code is publicly available: https://github.com/ajevnisek/optimize-and-reduce

AAAI Conference 2020 Conference Paper

Proximity Preserving Binary Code Using Signed Graph-Cut

• Inbal Lavi
• Shai Avidan
• Yoram Singer
• Yacov Hel-Or

We introduce a binary embedding framework, called Proximity Preserving Code (PPC), which learns similarity and dissimilarity between data points to create a compact and affinity-preserving binary code. This code can be used to apply fast and memory-efficient approximation to nearest-neighbor searches. Our framework is flexible, enabling different proximity definitions between data points. In contrast to previous methods that extract binary codes based on unsigned graph partitioning, our system models the attractive and repulsive forces in the data by incorporating positive and negative graph weights. The proposed framework is shown to boil down to finding the minimal cut of a signed graph, a problem known to be NP-hard. We offer an efficient approximation and achieve superior results by constructing the code bit after bit. We show that the proposed approximation is superior to the commonly used spectral methods with respect to both accuracy and complexity. Thus, it is useful for many other problems that can be translated into signed graph cut.

NeurIPS Conference 2006 Conference Paper

Efficient Methods for Privacy Preserving Face Detection

• Shai Avidan
• Moshe Butman

Bob offers a face-detection web service where clients can submit their images for analysis. Alice would very much like to use the service, but is reluctant to reveal the content of her images to Bob. Bob, for his part, is reluctant to release his face detector, as he spent a lot of time, energy and money constructing it. Secure Multi- Party computations use cryptographic tools to solve this problem without leaking any information. Unfortunately, these methods are slow to compute and we intro- duce a couple of machine learning techniques that allow the parties to solve the problem while leaking a controlled amount of information. The first method is an information-bottleneck variant of AdaBoost that lets Bob find a subset of features that are enough for classifying an image patch, but not enough to actually recon- struct it. The second machine learning technique is active learning that allows Alice to construct an online classifier, based on a small number of calls to Bob’s face detector. She can then use her online classifier as a fast rejector before using a cryptographically secure classifier on the remaining image patches.

ICML Conference 2006 Conference Paper

Generalized spectral bounds for sparse LDA

• Baback Moghaddam
• Yair Weiss
• Shai Avidan

NeurIPS Conference 2005 Conference Paper

Spectral Bounds for Sparse PCA: Exact and Greedy Algorithms

• Baback Moghaddam
• Yair Weiss
• Shai Avidan

Sparse PCA seeks approximate sparse "eigenvectors" whose projections capture the maximal variance of data. As a cardinality-constrained and non-convex optimization problem, it is NP-hard and is encountered in a wide range of applied fields, from bio-informatics to finance. Recent progress has focused mainly on continuous approximation and convex relaxation of the hard cardinality constraint. In contrast, we consider an alternative discrete spectral formulation based on variational eigenvalue bounds and provide an effective greedy strategy as well as provably optimal solutions using branch-and-bound search. Moreover, the exact methodology used reveals a simple renormalization step that improves approximate solutions obtained by any continuous method. The resulting performance gain of discrete algorithms is demonstrated on real-world benchmark data and in extensive Monte Carlo evaluation trials.

NeurIPS Conference 2004 Conference Paper

The power of feature clustering: An application to object detection

• Shai Avidan
• Moshe Butman

We give a fast rejection scheme that is based on image segments and demonstrate it on the canonical example of face detection. However, in- stead of focusing on the detection step we focus on the rejection step and show that our method is simple and fast to be learned, thus making it an excellent pre-processing step to accelerate standard machine learning classifiers, such as neural-networks, Bayes classifiers or SVM. We de- compose a collection of face images into regions of pixels with similar behavior over the image set. The relationships between the mean and variance of image segments are used to form a cascade of rejectors that can reject over 99. 8% of image patches, thus only a small fraction of the image patches must be passed to a full-scale classifier. Moreover, the training time for our method is much less than an hour, on a standard PC. The shape of the features (i. e. image segments) we use is data-driven, they are very cheap to compute and they form a very low dimensional feature space in which exhaustive search for the best features is tractable.